Iraklis Lazakis

Maintenance/repair and production-oriented life cycle cost/earning model for ship structural optimisation during conceptual design stage

The aim of this paper is to investigate the effect of the change in structural weight due to optimisation experiments on life cycle cost and earning elements using the life cycle cost/earning model, which was developed for structure optimisation. The relation between structural variables and relevant cost/earning elements are explored and discussed in detail. The developed model is restricted to the relevant life cycle cost and earning elements, namely production cost, periodic maintenance cost, fuel oil cost, operational earning and dismantling earning. Therefore it is important to emphasise here that the cost/earning figure calculated through the developed methodology will not be a full life cycle cost/earning value for a subject vessel, but will be the relevant life cycle cost/earning value. As one of the main focuses of this paper is the maintenance/repair issue, the data was collected from a number of ship operators and was solely used for the purpose of regression analysis. An illustrative example for a chemical tanker is provided to show the applicability of the proposed approach.

Increasing ship operational reliability through the implementation of a holistic maintenance management strategy

Ship maintenance was initially considered as more of a financial burden than as a way to preserve safety, environment and quality transportation. The benefits from applying a sound and systematic maintenance policy are emerging both in the minimisation of unnecessary downtime as well as in the increase of operational capability. In this paper, a novel predictive maintenance strategy is demonstrated, combining the existing ship operational and maintenance tasks with the advances stemming from new applied techniques. The initial step for the application of the above-mentioned strategy is also shown regarding the machinery space of a cruise ship. Well-known tools are applied such as Failure Modes, Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA). Outcomes of this study are the identification of the critical components of the system, the estimation of the reliability of the overall system and sub-systems, the prioritisation of the maintenance tasks and finally the availability of the specific end events/items.

Investigating the reliability and criticality of the maintenance characteristics of a diving support vessel

Maintenance tasks and their application in the shipping industry have evolved significantly in the recent years. Particularly in the offshore industry, safety onboard, environmental protection and intensive operational activities necessitate the minimization of down-time and the preservation of an excellent performance ratio. The first step of an innovative ship maintenance strategy, which is proposed by the authors and is based on criticality and reliability assessment, is presented herein using the FTA tool with time-dependant dynamic gates so as to represent in an accurate and comprehensive way the interrelation of the components of a system. The paper also presents a review of the maintenance standards and procedures, such as the ALARP concept, the Key Programme 3-Asset Integrity (KP3) initiative, the OREDA handbook as well as the RCM and RBI principles. As part of the reliability assessment, the Birnbaum and Criticality reliability importance measures are utilized to validate the results of the analysis. A case study of a diving support vessel (DSV) illustrates the application of this strategy. The main systems examined are: the vessels power plant, propulsion, water system, lifting, hauling and anchoring, diving and finally the safety system. The reliability of the main systems and subsystems as well as of their critical components is identified and suggestions of how to improve the overall reliability of the various systems both at a component, system and managerial level are also proposed.

Investigation of Optimum Crew Transfer Vessel Fleet for Offshore Wind Farm Maintenance Operations

The offshore wind industry, which aims to reduce the operational costs, usually achieved through learning curves and supply chain improvements, has seen drastic cost increase over the last five years. In order to sustain the competitiveness of the offshore wind industry against other renewable energy sources, the cost of offshore wind needs to come down to todays onshore cost. This cost reduction target can be achieved through optimising the offshore related operations which contribute the most to the operating expenditures (OPEX) of the offshore wind farms. In this paper, the investigation of optimum crew transfer vessel fleet, which indicates the influence of fleet size and characteristics of the vessels involved in the operations, is introduced with a focus on power production, total cost of the Operation and Maintenance (O&M) and revenue loss. A time domain Monte-Carlo approach is adopted while taking into consideration the climate parameters, failure characteristics of turbine components, the specification of crew transfer vessels, and the composition of vessel fleet. Through this extensive study, it is concluded the O&M related costs can be reduced significantly while the availability and the productivity of the turbines can be increased by optimising the use of the O&M vessel fleet in terms of fleet size and vessel capabilities.

Optimizing Ship Routing to Maximize Fleet Revenue at Danaos

In this paper we present an innovative toolkit that Danaos Corporation developed and deployed to optimize ship routing. Operations Research In Ship MAnagement ORISMA provides a clear answer to the conventional dilemma of least-cost voyage versus faster voyage. ORISMA maximizes revenue by using relevant information, including financial data, hydrodynamic models, weather conditions, and marketing forecasts. It considers the financial benefits after ship voyage completion to optimize the fleetwide performance instead of single-vessel performance. Using operations research and expert knowledge, we developed ORISMA to include world-class capabilities in scheduling optimization, intelligent voyage planning, ship bunkering, and chartering. In addition to maximizing Danaos’ profit, it helps the company to minimize carbon emissions, reduce staff workload, and increase customer satisfaction.

Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment:

Many maintenance approaches have been developed and applied successfully in a variety of sectors such as aviation and nuclear industries over the years. Some of those have also been employed in the maritime industry such as condition-based maintenance; however, choosing the best maintenance approach has always been a big challenge due to the involvement of many attributes and alternatives which can also be associated with multiple experts and vague information. In order to accommodate these aspects, and as part of an overall novel Reliability and Criticality Based Maintenance strategy, an existing fuzzy multiple attributive group decision-making technique is employed in this study, which is further enhanced with the use of Analytical Hierarchy Process to obtain a better weighting of the maintenance attributes used. The fuzzy multiple attributive group decision-making technique has three distinctive stages, namely rating, aggregation and selection in which multiple experts’ subjective judgments are processed and aggregated to be able to arrive at a ranking for a finite number of maintenance options. To demonstrate the applicability in a real-life industrial context, the technique is exemplified by selecting the best maintenance approach for shipboard equipment such as the diesel generator system of a vessel. The results denote that preventive maintenance is the best approach closely followed by predictive maintenance, thus steering away from the ship corrective maintenance framework and increasing overall ship system reliability and availability.

Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications

ABSTRACT Condition monitoring is the process of monitoring parameters expressing machinery condition, interpreting them for the identification of change which could indicate developing faults. Data processing is important in a ship condition monitoring software tool, as misinterpretation of data can significantly affect the accuracy and performance of the predictions made. Data for key performance parameters for a PANAMAX container ship main engine cylinder are clustered using a two-stage approach. Initially, the data is clustered using the artificial neural network (ANN)-self-organising map (SOM) and then the clusters are interclustered using the Euclidean distance metric into groups. The case study results demonstrate the capability of the SOM to monitor the main engine condition by identifying clusters containing data which are diverse compared to data representing normal engine operating conditions. The results obtained can be further expanded for application in diagnostic purposes, identifying faults, their causes and effects to the ship main engine.

Investigating an SVM-driven, one-class approach to estimating ship systems condition

ABSTRACT Maintenance is a major point that can affect vessel operation sustainability and profitability. Recent literature has shown that condition monitoring of ship systems shows great potential, albeit at significant data requirement costs. In this respect, this paper presents a novel methodology for intelligent, system-level engine performance monitoring, utilising noon-report data with minimal data assumptions. The proposed methodology is based on the training of a one-class Support Vector Machine, which models a diesel generator’s normal behaviour. Unseen data are then input into the model, where its output reflects a gauge of their normality, compared to the training dataset. This aids the dynamic detection of ship machinery incipient faults, contributing to the minimisation of ship downtime. A case study presenting applications of this modelling approach on ship machinery raw data is included, complemented by a sensitivity analysis. This demonstrates the applicability of the developed methodology in identifying deviant, abnormal ship machinery conditions.

Assessing offshore wind turbine reliability and availability

The renewables sector and particularly offshore wind energy is a fast developing industry over the last few years. Especially, activities related to the installation, and operation and maintenance of offshore wind turbines become a challenging task with inherent risks. This article assesses the risks related to the above stages of a wind farm lifecycle using the failure mode, effects and criticality analysis and hazard identification methods. All works, from installation to operation and maintenance, are considered together with the wind turbine main components. An integrated risk analysis methodology is presented addressing personnel Safety (S), Environmental impact (E), Asset integrity (A), and Operation (O). The above is supplemented by a cost analysis with the aid of Bayesian belief networks method to assist the decision-making process related to installation, and operation and maintenance tasks. All major risks and critical wind turbine components are identified as well as measures are suggested to prevent or mitigate them. Moreover, inspection and maintenance plans are elaborated in general for the mentioned activities.

An intelligent system for vessels structural reliability evaluation

An intelligent system is proposed within INCASS (Inspection Capabilities for Enhanced Ship Safety) project for evaluating ship structural reliability and assisting in fatigue damage and structure response assessment. The system combines hydrodynamic, finite element and structural reliability models.. The hydrodynamic analysis model is not discussed in this paper. The finite element model input is a mesh for the mid-ship part of the vessel. Finally, the in-house structural reliability model input is the calculated output of the previous model as well as models for estimating crack development and propagation, corrosion growth and fatigue loading. The output includes the probability of failure for all the investigated components versus time which can be used to assess safe operation through the developed decision support software. The database can receive information from various sources including inspection and robotic systems data. The case study of a capsize bulk carrier the presents structural evaluation process.